Distributed least square solution method to linear algebraic equations over multiagent networks
This paper designs a distributed least square solution method for a linear algebraic equation over a multiagent network. The coefficient matrix is divided into multiple blocks, and each agent only knows a subset of these blocks. The designed method is discrete-time and based on a proximal ADMM algorithm. By applying the designed method, each agent can find its corresponding part in one least square solution of the considered linear algebraic equation while using only its information and communicating with its neighbors. Numerical simulations verify the effectiveness of the designed method in MATLAB.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Scalable Distributed Least Squares Algorithm for Linear Algebraic Equations via Scheduling
In this work, we propose a novel discrete-time distributed algorithm for finding least squares solutions of linear algebraic equations with a scheduling protocol to further enhance its scalability. Each agent in the netw…
SchedulingA Resilient Distributed Algorithm for Solving Linear Equations
This paper presents a resilient distributed algorithm for solving a system of linear algebraic equations over a multi-agent network in the presence of Byzantine agents capable of arbitrarily introducing untrustworthy inf…
Adjusted least squares fitting of algebraic hypersurfaces
We consider the problem of fitting a set of points in Euclidean space by an algebraic hypersurface. We assume that points on a true hypersurface, described by a polynomial equation, are corrupted by zero mean independent…
Camera Pose Estimation from Lines using Plücker Coordinates
Correspondences between 3D lines and their 2D images captured by a camera are often used to determine position and orientation of the camera in space. In this work, we propose a novel algebraic algorithm to estimate the …
Camera Pose EstimationPose EstimationPositionDeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain Decomposition
We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks. Nonlinear least squares are commonly solved with the Levenberg-Marquardt (LM) algorithm for fast converg…
Deep LearningDistributed Optimization